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import gradio as gr |
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import os |
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os.system('pip install transformers torch') |
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from transformers import GPT2LMHeadModel, GPT2Tokenizer |
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model_name = "microsoft/DialoGPT-small" |
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model = GPT2LMHeadModel.from_pretrained(model_name) |
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tokenizer = GPT2Tokenizer.from_pretrained(model_name) |
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system_prompt = "You are a helpful assistant." |
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chat_history = system_prompt |
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def generate_response(prompt, max_length=50, temperature=0.8): |
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global chat_history |
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input_text = chat_history + " User: " + prompt |
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input_ids = tokenizer.encode(input_text, return_tensors="pt") |
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output_ids = model.generate(input_ids, max_length=max_length, temperature=temperature, num_return_sequences=1) |
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response = tokenizer.decode(output_ids[0], skip_special_tokens=True) |
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chat_history += f" User: {prompt} Assistant: {response}" |
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return response |
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iface = gr.Interface( |
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fn=generate_response, |
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inputs=gr.Textbox(), |
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outputs="text", |
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) |
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iface.launch() |
